CN109099964A - Mechanical seal end surface state monitoring method - Google Patents
Mechanical seal end surface state monitoring method Download PDFInfo
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- CN109099964A CN109099964A CN201810754424.5A CN201810754424A CN109099964A CN 109099964 A CN109099964 A CN 109099964A CN 201810754424 A CN201810754424 A CN 201810754424A CN 109099964 A CN109099964 A CN 109099964A
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- face
- mechanical seal
- end surface
- layer
- texturing
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
Abstract
The invention discloses mechanical seal end surface state monitoring method, method includes the following steps: step S1: being sealed to machinery；Step S2: the pattern of seal face is determined；Step S3: texturing processing is carried out according to surface of the pattern of seal face to mechanical seal end surface；Step S4: the texturing parameter under selection mechanical seal operation demand optimum state is utilized respectively DLC coating end face and the compound same seal face of texturing end face processing；Step S5: it carries out Experimental Comparison and analyzes the frictional behavior between untreated end face, DLC coating end face and compound texturing end face；Solve existing sealing premature failure, caused by downtime is long, enterprises' loss is big problem.
Description
Technical field
The present invention relates to mechanical seal end surface detection fields, especially mechanical seal end surface state monitoring method.
Background technique
Rotating machinery, such as: compressor, steam turbine, gas turbine, the hydraulic turbine, centrifugal pump are modern industrial production processes
In key equipment.The processing medium of such mechanical equipment processing usually has inflammable, explosive, toxic, high temperature and corrosivity.For
It prevents or limitation processing medium is leaked in natural environment along the shaft end of rotating machinery, it is necessary to use effective and reliable shaft end
Mechanical seal (Mechanical Seal) device, while must also consider that selected axle end sealing device is able to satisfy modern work
Industry is environmentally protective, pollution-free, long-life, high benefit and the requirement for completing energy-saving and emission-reduction index etc..According to English, beauty, De Deng state
Statistics, in petrochemical industry equipment, centrifugation pump accounts for 85% or more of whole pump equipment, and is centrifuged pump maintenance mans
Work measure 70% or so be solve the problems, such as sealing cause.
Such as in the entire energy consumption of China's petrochemical industry, the large percentage that various fluid dynamic machinery occupy, these machines
The interior leakage or leakage of tool often result in volumetric efficiency reduction, and power loss reaches 10% or more of general power, and small-sized machine is even
It is also possible to pollution environment simultaneously up to 40-50%.Novel mechanical seal form is studied, can not only reduce processing medium because of leakage
And the material loss generated, energy required for overcoming mechanical seal end surface to rub can also be reduced.Therefore, further investigation is mechanical
Sealing can ensure sealing element itself and corollary system reliability of operation and stability, reduce the consumption of material and the energy, always
It is an important topic in equipment of industrial product technology.
Compared with common fillers sealing, mechanical seal is with leakage rate is low, long service life, driving power consumption is low and does not wear
The features such as shaft.But because of complex structural designs, especially rubbing surface need to make micro-scale surface processing, and processing technology is more demanding etc.
Reason, mechanical seal product needed for leading to each relevant industries in China is by the whole world a few major company (such as Flowserve, John
Crane, Eagle Burgmann) it is monopolized.Meanwhile external mechanical seal product is compared, homemade goods high parameter field also
There are larger gaps.By taking dry gas seals product (a kind of non-contacting mechanical seal of surface of friction pair) as an example, the production of foreign countries' exploitation
Product are using pressure up to 45MPa, and linear velocity 230m/s, homemade goods maximum working (operation) pressure (MWP) is 10MPa, linear velocity 150m/s with
It is interior.
In order to improve domestic machinery sealing element operating parameter, the product of China's independent research is made to meet various application environments
Demand must just carry out further investigation in terms of mechanical seal mechanism, simulation calculation, status monitoring.In addition, domestic machinery seals
Although product can reach preferable effect in actual use, but often there is the case where sealing premature failure.
The reason of in order to find sealing premature failure, downtime is reduced, reduces enterprises' loss, mechanical seal mechanism must be just unfolded
Deeper into research.
In conclusion the mechanical seal market demand is big, have a extensive future.Improve China's mechanical seal independent research water
It is flat, reinforce mechanical seal theoretical research, to improving existing machinery Seal Design, shorten product development cycle, quickly flexibly ring
Answer user demand significant.Particularly, in more microcosmic point research seal face in system start and stop and steady operational process
Middle pressure field, thermal field, Flow Field Distribution carry out multi- scenarios method analysis to mechanical seal, will such as axial float, it is angular swing etc. it is dry
It disturbs factor and Accurate Analysis model is added, the above perspective study is carried out to mechanical seal mechanism, China's mechanical seal skill can be made
Art further shortens the gap between developed country, is preferably national economy service.
Summary of the invention
To solve problems of the prior art, the present invention provides mechanical seal end surface state monitoring methods, solve
Existing sealing premature failure, caused by downtime is long, enterprises' loss is big problem.
The technical solution adopted by the present invention is that mechanical seal end surface state monitoring method, method include the following steps:
Mechanical seal end surface state monitoring method, method include the following steps:
Step S1: machinery is sealed；
Step S2: the pattern of seal face is determined；
Step S3: texturing processing is carried out according to surface of the pattern of seal face to mechanical seal end surface；
Step S4: the texturing parameter under selection mechanical seal operation demand optimum state is utilized respectively DLC coating end face
With the compound same seal face of texturing end face processing；
Step S5: it carries out Experimental Comparison and analyzes rubbing between untreated end face, DLC coating end face and compound texturing end face
Wipe characteristic；
Step S6: analyzing the Experimental Comparison result of three kinds of obtained frictional behaviors of step S5, and is tied according to analysis
Fruit carries out signal data acquisition；
Step S7: using blind source separating and variation mode decomposition method to the signal extraction validity feature value of acquisition；
Step S8: according to the validity feature value extracted, using RBF network model and B-spline network model to abrasion shape
State is identified；
Step S9: output recognition result completes the monitoring to mechanical seal end surface state, terminates program.
Mechanical seal end surface state monitoring method of the present invention has the beneficial effect that:
1. screening is for the texturing parameter and DLC coating layer thickness under mechanical seal operation demand optimum state, by two kinds
Processing mode combines, and handles same seal face, carries out Experimental Comparison later and analyzes untreated end face, texturing end face, DLC
Frictional behavior between coating end face and compound texturing DLC coating end face.
2. the signal processing methods such as blind source separating and variation mode decomposition are introduced and extract effectively spy by the signal of pair acquisition
Sign；On the basis of studying vibration information, acoustic emission information and ultrasound information, by establishing B-spline neural network to close
Envelope state is monitored, and studying has distinct novelty.
Detailed description of the invention
Fig. 1 is the general flow chart of mechanical seal end surface state monitoring method of the present invention.
Fig. 2 is the RBF network architecture schematic diagram of mechanical seal end surface state monitoring method of the present invention.
Fig. 3 is the B-spline neural network structure schematic diagram of mechanical seal end surface state monitoring method of the present invention.
Specific embodiment
A specific embodiment of the invention is described below, in order to facilitate understanding by those skilled in the art this hair
It is bright, it should be apparent that the present invention is not limited to the ranges of specific embodiment, for those skilled in the art,
As long as various change is in the spirit and scope of the present invention that the attached claims limit and determine, these variations are aobvious and easy
See, all are using the innovation and creation of present inventive concept in the column of protection.
As shown in Figure 1, mechanical seal end surface state monitoring method, method include the following steps:
Step S1: machinery is sealed；
Step S2: the pattern of seal face is determined；
Step S3: texturing processing is carried out according to surface of the pattern of seal face to mechanical seal end surface；
Step S4: the texturing parameter under selection mechanical seal operation demand optimum state is utilized respectively DLC coating end face
With the compound same seal face of texturing end face processing；
Step S5: it carries out Experimental Comparison and analyzes rubbing between untreated end face, DLC coating end face and compound texturing end face
Wipe characteristic；
Step S6: analyzing the Experimental Comparison result of three kinds of obtained frictional behaviors of step S5, and is tied according to analysis
Fruit carries out signal data acquisition；
Step S7: using blind source separating and variation mode decomposition method to the signal extraction validity feature value of acquisition；
Step S8: according to the validity feature value extracted, using RBF network model and B-spline network model to abrasion shape
State is identified；
Step S9: output recognition result completes the monitoring to mechanical seal end surface state, terminates program.
The result of the step S8 of this programme is as follows:
RBF neural is 93.8% in the discrimination of pressure medium 0.2MPa, the identification under 0.5MPa pressure medium
Rate is 90.6%, and the discrimination under 1MPa pressure medium is 87.5%；
Discrimination of the B-spline neural network in 0.2Mpa is 91.5%, and the discrimination under 0.5MPa pressure medium is
92.6%, the discrimination under 1MPa pressure medium is 93.8%.
The characteristic extraction step of the blind source separating of the step S7 of this programme is as follows:
Step A1: s=(s is set1,s2,…,sn)TIt is the independent source of n zero-mean, x=(x1,x2,…,xm)TIt is m
Observation signal, observation signal are formed by independent source linear hybrid, and A is mxn rank full rank hybrid matrix, are not considering that additivity is made an uproar
In the case where sound, ICA mixed model is represented by x=As；
Step A2: a separation matrix W is found, it is independent as far as possible through the transformed output y of W to make observation signal x, and be
The estimation of source signal:
Y=Wx.
The characteristic extraction step of the variation mode decomposition method of step S7 is as follows:
Step B1: original signal f is decomposed into K basic friction angle component, the expression formula corresponding to VMD model are as follows:
In formula, { uk}={ u1,…,uKIt is to decompose K obtained Intrinsic mode functions；{ωk}={ ω1,…,ωKTable
Show that the center frequency of each component, σ (t) are dirac (Dirac) function, * represents convolution, and f is original signals and associated noises.
Step B2: introducing secondary penalty factor a and Lagrange multiplier operator λ, by restrictive variational problem become it is non-about
Beam variational problem, wherein secondary penalty factor can guarantee the reconstruction accuracy of signal, glug in the presence of Gaussian noise
Bright day operator makes constraint condition keep stringency, to obtain the optimal solution of problem:
In formula, a is secondary penalty factor, and λ (t) is Lagrange multiplier operator.
The texturing parameter of the step S4 of this programme includes texture aspect ratio, texture features and texture density.
As shown in Fig. 2, the RBF network model expression formula of the step S8 of this programme are as follows:
In formula, W is connection weight vector, W=(ωk1,ωk2,…ωkm), Φ is hidden layer output,
As shown in figure 3, the B-spline network model of the step S8 of this programme is
Ith layer is input layer,
In formula, i=1,2 ..., n；N is input number.
IIth layer is blurring layer, using B-spline function as membership function,
In formula,It is the basic function of j-th of fuzzy set, i=1,2 ..., n；J=1,2 ..., h, h are B-spline basic function
Number；K is the order of B-spline function；
IIIth layer is rules layer,
In formula, ki=1,2 ... h, " * " are product operations, indicate minimizing operation；
IVth layer is output layer,
In formula, l=1,2 ..., t；T is that network exports number；ωk1k2…knIndicate the connection weight of network.
The present embodiment is when implementing, the first step, carries out texturing, DLC coating respectively to mechanical seal end surface, screens needle
To the texturing parameter and DLC coating layer thickness under mechanical seal operation demand optimum state, two kinds of processing modes are combined, are located
Same seal face is managed, Experimental Comparison is carried out later and analyzes between untreated end face, DLC coating end face and compound texturing end face
Frictional behavior；
The signal processing methods such as blind source separating and variation mode decomposition are introduced and are extracted to the signal of acquisition by second step
Validity feature；
Useful signal after blind source separating is extracted into following characteristics:
Root mean square (RMS):
Energy variance (EV):
Peak factor (PAR):
xpeakFor the peak value of signal
Centre frequency (CF):
piIt is signal in frequency fiWhen amplitude
Flexure (AS):
xmeanFor the average value of signal
After the processing of features described above parameter normalization, the results are shown in Table 1:
1 normalization characteristic of table
After VMD is decomposed, the percentage E of gross energy shared by preceding 5 IMF components is extractedi/ E, gross energy
The results are shown in Table 2.
Table 2
Third step passes through RBF network mould on the basis of studying vibration information, acoustic emission information and ultrasound information
Type and B-spline network model identify state of wear, obtain report result.
RBF network model expression formula are as follows:
In formula, W is connection weight vector, W=(ωk1,ωk2,…ωkm), Φ is hidden layer output,
B-spline network model is
Ith layer is input layer,
In formula, i=1,2 ..., n；N is input number.
IIth layer is blurring layer, using B-spline function as membership function,
In formula,It is the basic function of j-th of fuzzy set, i=1,2 ..., n；J=1,2 ..., h, h are B-spline basic function
Number；K is the order of B-spline function；
IIIth layer is rules layer,
In formula, ki=1,2 ... h, " * " are product operations, indicate minimizing operation；
IVth layer is output layer,
In formula, l=1,2 ..., t；T is that network exports number；ωk1k2…knIndicate the connection weight of network.
RBF neural is 93.8% in the discrimination of pressure medium 0.2MPa, the identification under 0.5MPa pressure medium
Rate is 90.6%, and the discrimination under 1MPa pressure medium is 87.5%.
Discrimination of the B-spline neural network in 0.2Mpa is 91.5%, and the discrimination under 0.5MPa pressure medium is
92.6%, the discrimination under 1MPa pressure medium is 93.8%.
Claims (6)
1. mechanical seal end surface state monitoring method, which is characterized in that method includes the following steps:
Step S1: machinery is sealed；
Step S2: the pattern of seal face is determined；
Step S3: texturing processing is carried out according to surface of the pattern of seal face to mechanical seal end surface；
Step S4: the texturing parameter under selection mechanical seal operation demand optimum state is utilized respectively DLC coating end face and answers
Close the same seal face of texturing end face processing；
Step S5: it carries out Experimental Comparison and analyzes the spy of the friction between untreated end face, DLC coating end face and compound texturing end face
Property；
Step S6: analyzing the Experimental Comparison result of three kinds of obtained frictional behaviors of step S5, and based on the analysis results into
Row signal data acquisition；
Step S7: using blind source separating and variation mode decomposition method to the signal extraction validity feature value of acquisition；
Step S8: according to the validity feature value extracted, using RBF network model and B-spline network model to state of wear into
The identification of row feature；
Step S9: output recognition result completes the monitoring to mechanical seal end surface state, terminates program.
2. mechanical seal end surface state monitoring method according to claim 1, which is characterized in that the blind source of the step S7
Isolated characteristic extraction step is as follows:
Step A1: s=(s is set1,s2,…,sn)TIt is the independent source of n zero-mean, x=(x1,x2,…,xm)TIt is observed for m
Signal, observation signal are formed by independent source linear hybrid, and A is m × n rank full rank hybrid matrix, are not considering additive noise
In the case where, ICA mixed model is represented by x=As；
Step A2: finding a separation matrix W, keep observation signal x independent as far as possible through the transformed output y of W, and believes for source
Number estimation:
Y=Wx.
3. mechanical seal end surface state monitoring method according to claim 1, which is characterized in that the variation of the step S7
The characteristic extraction step of mode decomposition method is as follows:
Step B1: original signal f is decomposed into K basic friction angle component, the expression formula corresponding to VMD model are as follows:
In formula, { uk}={ u1,…,uKIt is to decompose K obtained Intrinsic mode functions；{ωk}={ ω1,…,ωKIndicate each
The center frequency of a component, σ (t) are dirac (Dirac) function, and * represents convolution, and f is original signals and associated noises.
Step B2: introducing secondary penalty factor a and Lagrange multiplier operator λ, and restrictive variational problem is become non-binding
Variational problem, wherein secondary penalty factor can guarantee the reconstruction accuracy of signal in the presence of Gaussian noise, Lagrange
Operator makes constraint condition keep stringency, to obtain the optimal solution of problem:
In formula, a is secondary penalty factor, and λ (t) is Lagrange multiplier operator.
4. mechanical seal end surface state monitoring method according to claim 1, which is characterized in that the texture of the step S4
Changing parameter includes texture aspect ratio, texture features and texture density.
5. mechanical seal end surface state monitoring method according to claim 1, which is characterized in that the RBF of the step S8
Network model expression formula are as follows:
In formula, W is connection weight vector, W=(ωk1,ωk2,…ωkm), Φ is hidden layer output,
6. mechanical seal end surface state monitoring method according to claim 1, which is characterized in that the B sample of the step S8
Network model is
Ith layer is input layer,
In formula, i=1,2 ..., n；N is input number.
IIth layer is blurring layer, using B-spline function as membership function,
In formula,It is the basic function of j-th of fuzzy set, i=1,2 ..., n；J=1,2 ..., h, h are of B-spline basic function
Number；K is the order of B-spline function；
IIIth layer is rules layer,
In formula, ki=1,2 ... h, " * " are product operations, indicate minimizing operation；
IVth layer is output layer,
In formula, l=1,2 ..., t；T is that network exports number；ωk1k2…knIndicate the connection weight of network.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114644276A (en) * | 2022-04-11 | 2022-06-21 | 伊萨电梯有限公司 | Intelligent elevator control method under mixed scene condition |
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| CN116989937B (en) * | 2023-09-25 | 2023-12-22 | 苏州俊煌机械科技有限公司 | Detection method and device for mechanical sealing element |
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Application publication date: 20181228 |